Autoencoder: predictions missing for nodes in the bottleneck layer
My closest guess is because you are using the activation function ReLU, which pushes data to be greater than zero. However, because of your data's nature, the autoencoder is highly dependent on negative calculations to reconstruct your data, but the best it can achieve is zero.

In the context of artificial neural networks, the rectifier is an activation function defined as the positive part of its argument:
$$f(x) = x^+ = max(0, x)$$
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)